Overview

Dataset statistics

Number of variables15
Number of observations340
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.0 KiB
Average record size in memory120.4 B

Variable types

Unsupported1
Categorical2
Numeric12

Alerts

Symbol has constant value "RELIANCE" Constant
Series has constant value "EQ" Constant
Prev Close is highly correlated with Open and 6 other fieldsHigh correlation
Open is highly correlated with Prev Close and 6 other fieldsHigh correlation
High is highly correlated with Prev Close and 6 other fieldsHigh correlation
Low is highly correlated with Prev Close and 7 other fieldsHigh correlation
Last is highly correlated with Prev Close and 6 other fieldsHigh correlation
Close is highly correlated with Prev Close and 6 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 6 other fieldsHigh correlation
Volume is highly correlated with Low and 3 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Volume and 2 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 2 other fieldsHigh correlation
%Deliverble is highly correlated with Prev Close and 6 other fieldsHigh correlation
Prev Close is highly correlated with Open and 6 other fieldsHigh correlation
Open is highly correlated with Prev Close and 6 other fieldsHigh correlation
High is highly correlated with Prev Close and 6 other fieldsHigh correlation
Low is highly correlated with Prev Close and 6 other fieldsHigh correlation
Last is highly correlated with Prev Close and 6 other fieldsHigh correlation
Close is highly correlated with Prev Close and 6 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 6 other fieldsHigh correlation
Volume is highly correlated with Turnover and 2 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Volume and 2 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 2 other fieldsHigh correlation
%Deliverble is highly correlated with Prev Close and 6 other fieldsHigh correlation
Prev Close is highly correlated with Open and 5 other fieldsHigh correlation
Open is highly correlated with Prev Close and 5 other fieldsHigh correlation
High is highly correlated with Prev Close and 5 other fieldsHigh correlation
Low is highly correlated with Prev Close and 5 other fieldsHigh correlation
Last is highly correlated with Prev Close and 5 other fieldsHigh correlation
Close is highly correlated with Prev Close and 5 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 5 other fieldsHigh correlation
Volume is highly correlated with Turnover and 2 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Volume and 2 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 2 other fieldsHigh correlation
Symbol is highly correlated with SeriesHigh correlation
Series is highly correlated with SymbolHigh correlation
Prev Close is highly correlated with Open and 6 other fieldsHigh correlation
Open is highly correlated with Prev Close and 6 other fieldsHigh correlation
High is highly correlated with Prev Close and 6 other fieldsHigh correlation
Low is highly correlated with Prev Close and 6 other fieldsHigh correlation
Last is highly correlated with Prev Close and 6 other fieldsHigh correlation
Close is highly correlated with Prev Close and 6 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 6 other fieldsHigh correlation
Volume is highly correlated with Turnover and 2 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Volume and 2 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 2 other fieldsHigh correlation
%Deliverble is highly correlated with Prev Close and 6 other fieldsHigh correlation
Volume has unique values Unique
Turnover has unique values Unique
Trades has unique values Unique
Deliverable Volume has unique values Unique
Date is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-03-06 13:39:13.536889
Analysis finished2022-03-06 13:40:06.059852
Duration52.52 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size2.8 KiB

Symbol
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
RELIANCE
340 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRELIANCE
2nd rowRELIANCE
3rd rowRELIANCE
4th rowRELIANCE
5th rowRELIANCE

Common Values

ValueCountFrequency (%)
RELIANCE340
100.0%

Length

2022-03-06T19:10:06.430239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-06T19:10:06.604124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
reliance340
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Series
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
EQ
340 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEQ
2nd rowEQ
3rd rowEQ
4th rowEQ
5th rowEQ

Common Values

ValueCountFrequency (%)
EQ340
100.0%

Length

2022-03-06T19:10:06.769029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-06T19:10:06.939929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
eq340
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Prev Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct332
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2194.296471
Minimum1841.95
Maximum2731.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:07.171796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1841.95
5-th percentile1916.4825
Q11994.4125
median2122.65
Q32387.075
95-th percentile2572.67
Maximum2731.85
Range889.9
Interquartile range (IQR)392.6625

Descriptive statistics

Standard deviation223.7840358
Coefficient of variation (CV)0.1019844122
Kurtosis-1.013895162
Mean2194.296471
Median Absolute Deviation (MAD)166.05
Skewness0.4639493553
Sum746060.8
Variance50079.29469
MonotonicityNot monotonic
2022-03-06T19:10:07.466628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20862
 
0.6%
1964.052
 
0.6%
2627.42
 
0.6%
2700.42
 
0.6%
2425.62
 
0.6%
2378.32
 
0.6%
2227.42
 
0.6%
1911.152
 
0.6%
1992.61
 
0.3%
2403.91
 
0.3%
Other values (322)322
94.7%
ValueCountFrequency (%)
1841.951
0.3%
1850.41
0.3%
1876.551
0.3%
1877.451
0.3%
18951
0.3%
1895.31
0.3%
1897.251
0.3%
1899.51
0.3%
1901.151
0.3%
1901.71
0.3%
ValueCountFrequency (%)
2731.851
0.3%
2707.61
0.3%
2700.42
0.6%
2694.951
0.3%
2671.251
0.3%
2667.81
0.3%
2661.051
0.3%
2652.651
0.3%
2627.42
0.6%
2622.51
0.3%

Open
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct310
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2198.4025
Minimum1837
Maximum2742.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:07.772453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1837
5-th percentile1923.9675
Q11998.3125
median2126.15
Q32396.2875
95-th percentile2585.7375
Maximum2742.75
Range905.75
Interquartile range (IQR)397.975

Descriptive statistics

Standard deviation223.4423949
Coefficient of variation (CV)0.1016385284
Kurtosis-0.9701326711
Mean2198.4025
Median Absolute Deviation (MAD)166.35
Skewness0.4779398686
Sum747456.85
Variance49926.50385
MonotonicityNot monotonic
2022-03-06T19:10:08.072282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19803
 
0.9%
24523
 
0.9%
19902
 
0.6%
20822
 
0.6%
24302
 
0.6%
21742
 
0.6%
24002
 
0.6%
20582
 
0.6%
23732
 
0.6%
26202
 
0.6%
Other values (300)318
93.5%
ValueCountFrequency (%)
18371
0.3%
1859.41
0.3%
1880.151
0.3%
18901
0.3%
1892.251
0.3%
1894.31
0.3%
19031
0.3%
19041
0.3%
19061
0.3%
1910.51
0.3%
ValueCountFrequency (%)
2742.751
0.3%
2727.41
0.3%
2723.81
0.3%
2701.41
0.3%
2700.351
0.3%
26801
0.3%
26791
0.3%
2668.61
0.3%
26521
0.3%
26451
0.3%

High
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct323
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2221.109706
Minimum1905
Maximum2751.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:08.394101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1905
5-th percentile1940
Q12017.6125
median2148.9
Q32415.0125
95-th percentile2598.9225
Maximum2751.35
Range846.35
Interquartile range (IQR)397.4

Descriptive statistics

Standard deviation225.2508541
Coefficient of variation (CV)0.1014136553
Kurtosis-0.9962491107
Mean2221.109706
Median Absolute Deviation (MAD)174.425
Skewness0.4725853197
Sum755177.3
Variance50737.94728
MonotonicityNot monotonic
2022-03-06T19:10:08.674957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19973
 
0.9%
25202
 
0.6%
24332
 
0.6%
2079.42
 
0.6%
19652
 
0.6%
2598.752
 
0.6%
24542
 
0.6%
19692
 
0.6%
20582
 
0.6%
22352
 
0.6%
Other values (313)319
93.8%
ValueCountFrequency (%)
19051
0.3%
19091
0.3%
1913.11
0.3%
1914.451
0.3%
1916.41
0.3%
1918.91
0.3%
19191
0.3%
1923.31
0.3%
19251
0.3%
19291
0.3%
ValueCountFrequency (%)
2751.351
0.3%
27451
0.3%
2742.751
0.3%
27281
0.3%
27201
0.3%
2719.51
0.3%
2717.451
0.3%
2684.41
0.3%
26831
0.3%
26801
0.3%

Low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct322
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2171.517353
Minimum1830
Maximum2708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:08.988758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1830
5-th percentile1899.995
Q11980.1125
median2098.075
Q32366.25
95-th percentile2551.395
Maximum2708
Range878
Interquartile range (IQR)386.1375

Descriptive statistics

Standard deviation221.5457424
Coefficient of variation (CV)0.1020234732
Kurtosis-0.9983434663
Mean2171.517353
Median Absolute Deviation (MAD)166.075
Skewness0.4610880799
Sum738315.9
Variance49082.516
MonotonicityNot monotonic
2022-03-06T19:10:09.268617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19323
 
0.9%
23052
 
0.6%
24122
 
0.6%
24042
 
0.6%
2180.12
 
0.6%
20812
 
0.6%
2357.152
 
0.6%
24952
 
0.6%
25702
 
0.6%
19852
 
0.6%
Other values (312)319
93.8%
ValueCountFrequency (%)
18301
0.3%
1835.11
0.3%
18371
0.3%
18481
0.3%
1854.951
0.3%
1855.251
0.3%
1859.151
0.3%
18701
0.3%
1876.71
0.3%
18881
0.3%
ValueCountFrequency (%)
27081
0.3%
2691.51
0.3%
2687.21
0.3%
2669.31
0.3%
2662.31
0.3%
2645.351
0.3%
2641.251
0.3%
2619.951
0.3%
2611.51
0.3%
2603.21
0.3%

Last
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct324
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2194.673971
Minimum1844.95
Maximum2730.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:09.568445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1844.95
5-th percentile1917.8825
Q11993.9875
median2124.675
Q32387.6625
95-th percentile2572.125
Maximum2730.8
Range885.85
Interquartile range (IQR)393.675

Descriptive statistics

Standard deviation223.8214559
Coefficient of variation (CV)0.1019839206
Kurtosis-1.015571249
Mean2194.673971
Median Absolute Deviation (MAD)168.5
Skewness0.4611247091
Sum746189.15
Variance50096.04411
MonotonicityNot monotonic
2022-03-06T19:10:09.834275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21123
 
0.9%
20903
 
0.9%
20202
 
0.6%
21052
 
0.6%
21782
 
0.6%
2001.552
 
0.6%
19902
 
0.6%
22252
 
0.6%
23822
 
0.6%
19352
 
0.6%
Other values (314)318
93.5%
ValueCountFrequency (%)
1844.951
0.3%
1854.51
0.3%
18761
0.3%
1876.351
0.3%
18901
0.3%
1898.151
0.3%
18991
0.3%
19011
0.3%
1903.051
0.3%
1903.351
0.3%
ValueCountFrequency (%)
2730.81
0.3%
2707.81
0.3%
2703.51
0.3%
27011
0.3%
26981
0.3%
2669.21
0.3%
26681
0.3%
2666.31
0.3%
26551
0.3%
26351
0.3%

Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct332
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2194.8875
Minimum1841.95
Maximum2731.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:10.132104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1841.95
5-th percentile1916.4825
Q11994.4125
median2122.75
Q32387.075
95-th percentile2572.67
Maximum2731.85
Range889.9
Interquartile range (IQR)392.6625

Descriptive statistics

Standard deviation223.8647665
Coefficient of variation (CV)0.1019937316
Kurtosis-1.021387593
Mean2194.8875
Median Absolute Deviation (MAD)167.175
Skewness0.4561679608
Sum746261.75
Variance50115.4337
MonotonicityNot monotonic
2022-03-06T19:10:10.415961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20862
 
0.6%
2627.42
 
0.6%
2425.62
 
0.6%
2700.42
 
0.6%
2227.42
 
0.6%
2378.32
 
0.6%
1911.152
 
0.6%
1964.052
 
0.6%
1947.81
 
0.3%
21751
 
0.3%
Other values (322)322
94.7%
ValueCountFrequency (%)
1841.951
0.3%
1850.41
0.3%
1876.551
0.3%
1877.451
0.3%
18951
0.3%
1895.31
0.3%
1897.251
0.3%
1899.51
0.3%
1901.151
0.3%
1901.71
0.3%
ValueCountFrequency (%)
2731.851
0.3%
2707.61
0.3%
2700.42
0.6%
2694.951
0.3%
2671.251
0.3%
2667.81
0.3%
2661.051
0.3%
2652.651
0.3%
2627.42
0.6%
2622.51
0.3%

VWAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct339
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2196.346471
Minimum1871.72
Maximum2733.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:10.741778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1871.72
5-th percentile1923.0565
Q11997.565
median2119.685
Q32388.4575
95-th percentile2582.059
Maximum2733.67
Range861.95
Interquartile range (IQR)390.8925

Descriptive statistics

Standard deviation222.9967108
Coefficient of variation (CV)0.1015307529
Kurtosis-1.00976855
Mean2196.346471
Median Absolute Deviation (MAD)167.54
Skewness0.4652641279
Sum746757.8
Variance49727.53303
MonotonicityNot monotonic
2022-03-06T19:10:11.023614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1977.572
 
0.6%
2445.461
 
0.3%
2057.21
 
0.3%
2373.061
 
0.3%
2334.171
 
0.3%
1989.081
 
0.3%
2583.271
 
0.3%
2437.261
 
0.3%
1889.811
 
0.3%
2228.281
 
0.3%
Other values (329)329
96.8%
ValueCountFrequency (%)
1871.721
0.3%
1873.921
0.3%
1874.031
0.3%
1889.811
0.3%
1893.241
0.3%
1895.321
0.3%
1898.591
0.3%
1902.571
0.3%
1904.591
0.3%
1906.741
0.3%
ValueCountFrequency (%)
2733.671
0.3%
2713.781
0.3%
2710.541
0.3%
2700.841
0.3%
2691.351
0.3%
2682.211
0.3%
2661.51
0.3%
2652.281
0.3%
2642.911
0.3%
2642.811
0.3%

Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9091601.374
Minimum787160
Maximum45857806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:11.348428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum787160
5-th percentile3707343.9
Q15157476
median7267324
Q310921803.5
95-th percentile20183022.35
Maximum45857806
Range45070646
Interquartile range (IQR)5764327.5

Descriptive statistics

Standard deviation6135884.744
Coefficient of variation (CV)0.6748959278
Kurtosis9.164529952
Mean9091601.374
Median Absolute Deviation (MAD)2436452.5
Skewness2.521894898
Sum3091144467
Variance3.764908159 × 1013
MonotonicityNot monotonic
2022-03-06T19:10:11.921497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54006041
 
0.3%
46220021
 
0.3%
72746861
 
0.3%
172975751
 
0.3%
102401681
 
0.3%
39253451
 
0.3%
85213881
 
0.3%
59317741
 
0.3%
55007081
 
0.3%
66674831
 
0.3%
Other values (330)330
97.1%
ValueCountFrequency (%)
7871601
0.3%
18539481
0.3%
22167081
0.3%
24119001
0.3%
25020731
0.3%
28682351
0.3%
29243701
0.3%
29418831
0.3%
30837471
0.3%
30999561
0.3%
ValueCountFrequency (%)
458578061
0.3%
422096871
0.3%
409311701
0.3%
370031111
0.3%
307700801
0.3%
272857821
0.3%
265229721
0.3%
261784771
0.3%
260608641
0.3%
255463341
0.3%

Turnover
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.940903028 × 1015
Minimum1.965800892 × 1014
Maximum9.179980463 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:12.238316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.965800892 × 1014
5-th percentile8.126630322 × 1014
Q11.169255281 × 1015
median1.614175145 × 1015
Q32.289177608 × 1015
95-th percentile4.029350659 × 1015
Maximum9.179980463 × 1015
Range8.983400374 × 1015
Interquartile range (IQR)1.119922327 × 1015

Descriptive statistics

Standard deviation1.203974201 × 1015
Coefficient of variation (CV)0.6203165143
Kurtosis9.043913385
Mean1.940903028 × 1015
Median Absolute Deviation (MAD)4.849845464 × 1014
Skewness2.476696369
Sum6.599070294 × 1017
Variance1.449553876 × 1030
MonotonicityNot monotonic
2022-03-06T19:10:12.528152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.167604276 × 10151
 
0.3%
3.411408851 × 10151
 
0.3%
1.20127422 × 10151
 
0.3%
9.432810633 × 10141
 
0.3%
1.794404524 × 10151
 
0.3%
1.211333005 × 10151
 
0.3%
1.030073694 × 10151
 
0.3%
3.535832256 × 10151
 
0.3%
2.094509779 × 10151
 
0.3%
1.74432314 × 10151
 
0.3%
Other values (330)330
97.1%
ValueCountFrequency (%)
1.965800892 × 10141
0.3%
4.38805404 × 10141
0.3%
4.838163828 × 10141
0.3%
5.722594257 × 10141
0.3%
5.98656851 × 10141
0.3%
6.500981051 × 10141
0.3%
6.524718783 × 10141
0.3%
6.808700909 × 10141
0.3%
7.02822628 × 10141
0.3%
7.179873286 × 10141
0.3%
ValueCountFrequency (%)
9.179980463 × 10151
0.3%
8.839332015 × 10151
0.3%
7.670176154 × 10151
0.3%
7.005565561 × 10151
0.3%
6.20238801 × 10151
0.3%
5.878142158 × 10151
0.3%
5.387879159 × 10151
0.3%
5.380107704 × 10151
0.3%
5.308461085 × 10151
0.3%
5.101082167 × 10151
0.3%

Trades
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271170.8294
Minimum63285
Maximum1428490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:12.831976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum63285
5-th percentile137749.1
Q1189036.75
median230542.5
Q3297718.75
95-th percentile508015.2
Maximum1428490
Range1365205
Interquartile range (IQR)108682

Descriptive statistics

Standard deviation144285.0235
Coefficient of variation (CV)0.5320816541
Kurtosis15.00147885
Mean271170.8294
Median Absolute Deviation (MAD)53399.5
Skewness2.96224939
Sum92198082
Variance2.081816799 × 1010
MonotonicityNot monotonic
2022-03-06T19:10:13.125811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6476791
 
0.3%
2060021
 
0.3%
1466131
 
0.3%
2337201
 
0.3%
3038011
 
0.3%
1733931
 
0.3%
2464591
 
0.3%
3841881
 
0.3%
2838371
 
0.3%
2024311
 
0.3%
Other values (330)330
97.1%
ValueCountFrequency (%)
632851
0.3%
895091
0.3%
994841
0.3%
1051901
0.3%
1056191
0.3%
1060301
0.3%
1074751
0.3%
1143451
0.3%
1184691
0.3%
1218101
0.3%
ValueCountFrequency (%)
14284901
0.3%
9909351
0.3%
8889041
0.3%
7944361
0.3%
7552781
0.3%
7231941
0.3%
6592841
0.3%
6491961
0.3%
6476791
0.3%
6423841
0.3%

Deliverable Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3774321.718
Minimum402932
Maximum15742153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:13.460616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum402932
5-th percentile1582746.15
Q12425517
median3155871
Q34433174.25
95-th percentile7857436.4
Maximum15742153
Range15339221
Interquartile range (IQR)2007657.25

Descriptive statistics

Standard deviation2101818.051
Coefficient of variation (CV)0.5568730512
Kurtosis4.763680606
Mean3774321.718
Median Absolute Deviation (MAD)934292
Skewness1.807813646
Sum1283269384
Variance4.41763912 × 1012
MonotonicityNot monotonic
2022-03-06T19:10:13.793127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29970921
 
0.3%
86017791
 
0.3%
22544951
 
0.3%
12161611
 
0.3%
19005051
 
0.3%
24184471
 
0.3%
34065061
 
0.3%
29334881
 
0.3%
52256481
 
0.3%
28885481
 
0.3%
Other values (330)330
97.1%
ValueCountFrequency (%)
4029321
0.3%
5323281
0.3%
6214851
0.3%
10133141
0.3%
10540221
0.3%
10828651
0.3%
10893961
0.3%
11606721
0.3%
12161611
0.3%
12300921
0.3%
ValueCountFrequency (%)
157421531
0.3%
125968801
0.3%
124893381
0.3%
110646541
0.3%
98942471
0.3%
95871731
0.3%
94607171
0.3%
93345891
0.3%
92452551
0.3%
91068451
0.3%

%Deliverble
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct325
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.444795
Minimum0.1738
Maximum0.7185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-03-06T19:10:14.144944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1738
5-th percentile0.260645
Q10.36225
median0.4554
Q30.537775
95-th percentile0.61279
Maximum0.7185
Range0.5447
Interquartile range (IQR)0.175525

Descriptive statistics

Standard deviation0.1118064729
Coefficient of variation (CV)0.2513662988
Kurtosis-0.7748855732
Mean0.444795
Median Absolute Deviation (MAD)0.0883
Skewness-0.1545272182
Sum151.2303
Variance0.01250068738
MonotonicityNot monotonic
2022-03-06T19:10:14.473758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56222
 
0.6%
0.36722
 
0.6%
0.55212
 
0.6%
0.2932
 
0.6%
0.4842
 
0.6%
0.58772
 
0.6%
0.28572
 
0.6%
0.45762
 
0.6%
0.46782
 
0.6%
0.54372
 
0.6%
Other values (315)320
94.1%
ValueCountFrequency (%)
0.17381
0.3%
0.18011
0.3%
0.18381
0.3%
0.20191
0.3%
0.21921
0.3%
0.22331
0.3%
0.22511
0.3%
0.22921
0.3%
0.23651
0.3%
0.24571
0.3%
ValueCountFrequency (%)
0.71851
0.3%
0.68581
0.3%
0.66671
0.3%
0.65351
0.3%
0.6491
0.3%
0.64531
0.3%
0.63881
0.3%
0.63481
0.3%
0.63471
0.3%
0.63371
0.3%

Interactions

2022-03-06T19:09:59.982024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:21.990594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:26.416507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:30.229328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:33.315563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:36.668649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:39.628956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:42.709196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:45.707186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:49.186194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:52.491307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:56.154213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:00.339821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:23.096406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:26.734330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:30.494176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:33.573416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:36.913507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:39.879814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:42.946062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:45.964037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:49.488022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:52.773145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:56.451044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:00.662635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:23.444206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:27.019162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:30.760024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:33.815288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:37.149372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:40.124675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:43.185921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:46.212897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:49.773858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:53.056985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:56.749872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:00.962465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:23.700059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:27.294004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:31.013882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:34.332002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:37.374247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:40.364553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:43.412795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:46.448759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:50.017720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:53.324829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:57.069692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:01.298275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:23.997889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:27.622816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:31.289725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:34.589835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:37.619123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:40.619408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:43.654356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:46.706615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:50.263580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:53.605672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:57.364523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:01.658067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:24.259739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:27.916649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:31.528588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:34.819722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:37.848991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:40.854257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:43.878228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:46.950475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:50.509439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:53.881513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:57.659352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:02.321688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:24.541578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:28.223474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:31.768447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:35.065566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:38.081840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:41.101117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:44.145078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:47.500156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:50.757297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:54.158372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:57.949187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:02.645503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:24.794436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:28.516306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:32.001315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:35.308425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:38.302713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:41.338978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:44.368951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:47.735024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:50.995163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:54.426203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:58.229026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:03.024285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:25.106275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:28.874104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:32.242177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:35.558285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:38.546578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:41.588838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:44.619804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:47.992877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:51.286995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:54.873946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:58.536854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:03.342105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:25.408085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:29.249886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:32.489036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:35.809141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:38.784442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:41.845691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:44.864664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:48.246751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:51.590820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:55.180768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:58.831683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:03.715893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:25.740895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:29.605686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:32.768876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:36.100971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:39.071277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:42.135523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:45.146504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:48.533567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:51.905641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:55.515577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:59.161493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:10:04.092694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:26.096689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:29.947508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:33.064707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:36.406800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:39.367108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:42.439351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:45.448334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:48.871376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:52.223478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:55.860383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-06T19:09:59.580254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-03-06T19:10:14.766590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-06T19:10:15.361702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-06T19:10:15.888398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-06T19:10:16.381116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-06T19:10:16.678966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-06T19:10:04.638366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-06T19:10:05.493297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%Deliverble
02020-10-22RELIANCEEQ2124.602127.402132.502091.002111.902106.952107.04142152552.995212e+1539149858362810.4106
12020-10-23RELIANCEEQ2106.952106.002135.002096.402112.002113.052118.90108093832.290405e+1526518735515020.3286
22020-10-26RELIANCEEQ2113.052101.952101.952018.502034.902029.102052.69172253403.535832e+1558123863247760.3672
32020-10-27RELIANCEEQ2029.102034.902059.852005.002034.402034.502026.38168350113.411409e+1546276451997960.3089
42020-10-28RELIANCEEQ2034.502041.802057.702007.402008.902011.452026.02138100542.797944e+1541384948220480.3492
52020-10-29RELIANCEEQ2011.451997.002042.001991.002029.052026.902019.57141476812.857219e+1538208733461000.2365
62020-10-30RELIANCEEQ2026.902033.502065.102021.802064.352054.502040.66157011233.204061e+1534917738808070.2472
72020-11-02RELIANCEEQ2054.502027.002027.001859.151876.001877.451927.55458578068.839332e+151428490125968800.2747
82020-11-03RELIANCEEQ1877.451890.001909.001835.101854.501850.401873.92409311707.670176e+15990935110646540.2703
92020-11-04RELIANCEEQ1850.401837.001929.001837.001910.501913.201893.24370031117.005566e+1588890466658640.1801

Last rows

DateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%Deliverble
3302022-02-18RELIANCEEQ2443.502444.402456.402420.152427.302424.402440.2531268737.630367e+1415869112161610.3889
3312022-02-21RELIANCEEQ2424.402412.002437.502384.602399.602399.902409.1251259681.234908e+1522621428885480.5635
3322022-02-22RELIANCEEQ2399.902352.902395.902347.152389.752389.002372.1662279011.477358e+1519740139207890.6296
3332022-02-23RELIANCEEQ2389.002401.102406.552368.002371.252374.052391.2333736158.067102e+1419535019280540.5715
3342022-02-24RELIANCEEQ2374.052305.002339.902243.402250.002255.752291.15114667252.627201e+1549345972668770.6337
3352022-02-25RELIANCEEQ2255.752280.102307.952276.252283.002283.952290.8467240941.540384e+1528104736702870.5458
3362022-02-28RELIANCEEQ2283.952243.002367.352243.002355.002359.552328.6498287682.288768e+1524002053210300.5414
3372022-03-02RELIANCEEQ2359.552334.452401.002329.202399.252398.552376.6999092262.355116e+1530932862778290.6335
3382022-03-03RELIANCEEQ2398.552400.002414.852370.052382.002378.302392.2747011831.124648e+1523014324794490.5274
3392022-03-04RELIANCEEQ2378.302353.002364.302320.352327.952325.552338.2049868141.166015e+1521986027540950.5523